CN115508915B - Flood forecasting method, system, electronic equipment and storage medium - Google Patents
Flood forecasting method, system, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a flood forecasting method, a flood forecasting system, electronic equipment and a storage medium, and relates to the technical fields of hydrology and remote sensing, wherein the method comprises the following steps: acquiring fusion precipitation information of a target river basin at the current moment; the fusion precipitation information is obtained by fusion according to the multisource remote sensing precipitation information; according to the fusion precipitation information at the current moment and the state variable of the target river basin at the last moment, determining a flood forecast result of the target river basin at the current moment and the state variable of the target river basin at the current moment; the state variables include: water, evaporation capacity and soil humidity; the state variable at the initial moment is determined according to the remote sensing image of the target river basin at the initial moment; the remote sensing image includes: satellite images, radar images, and drone images. According to the invention, the rainfall information is fused, and the state variable of the last moment is adopted to infer the flood forecasting result of the current moment, so that the accuracy and precision of flood forecasting are improved.
Description
Technical Field
The invention relates to the technical field of hydrology and remote sensing, in particular to a flood forecasting method, a flood forecasting system, electronic equipment and a storage medium.
Background
The application of multi-source information in hydrology is an important trend of future development, the data fusion and assimilation can introduce the multi-source information into the hydrology field, and the multi-source information is a research field which is focused in recent years and rapidly developed, and especially the fusion and assimilation of remote sensing data in a hydrological model has become a hot spot of current hydrologic research. The hydrologic monitoring system is developed from original manual monitoring to an intelligent hydrologic monitoring system which mainly comprises satellite positioning, sky remote sensing, the Internet of things, intelligent sensing, mobile broadband, cloud computing, big data and the like. The intelligent hydrologic monitoring has three layers: space-based monitoring, air-based monitoring, land-based monitoring. The three-layer monitoring has the advantages and disadvantages, the space-based observation and the space-based observation are greatly influenced by weather and topography, and the land-based observation has insufficient representativeness of data and the like due to the problems of insufficient sites, uneven distribution and the like. Aiming at the problems existing in the observation, the multi-source data fusion and assimilation technology is adopted, the respective advantages of site data and satellite data are fully utilized to obtain key input information and state variables such as rainfall, soil humidity and the like with high resolution, and the forecasting precision of the refined hydrologic model is improved.
However, sky-ground multi-source precipitation fusion and basin state variable inversion assimilation are often applied as separate technologies or algorithms in flood forecast, and in the separate application process, the multi-source precipitation fusion, basin state variable inversion and basin state variable assimilation all generate different system errors or application errors, so that the accuracy and precision of calculation results are required to be further improved.
Disclosure of Invention
The invention aims to provide a flood forecasting method, a system, electronic equipment and a storage medium, which improve the accuracy and precision of flood forecasting.
In order to achieve the above object, the present invention provides the following solutions:
a flood forecasting method, the method comprising:
acquiring fusion precipitation information of a target river basin at the current moment; the fusion precipitation information is obtained by fusion according to multi-source remote sensing precipitation information, and the multi-source remote sensing precipitation information comprises: site precipitation information, radar precipitation information, satellite precipitation information and mode precipitation information;
according to the fusion precipitation information at the current moment and the state variable of the target river basin at the last moment, determining a flood forecast result of the target river basin at the current moment and the state variable of the target river basin at the current moment; the state variables include: water, evaporation capacity and soil humidity;
the state variable at the initial moment is determined according to the remote sensing image of the target river basin at the initial moment; the remote sensing image comprises: satellite images, radar images, and drone images.
Optionally, the acquiring the fused precipitation information of the target river basin at the current moment specifically includes:
acquiring multisource remote sensing precipitation information of a target river basin at the current moment;
removing null values in the multi-source remote sensing precipitation information at the current moment, and performing space-time resolution normalization processing to obtain a multi-source remote sensing precipitation data set with consistent space-time resolution at the current moment;
and fusing the multisource remote sensing precipitation data sets with consistent space-time resolution at the current moment by using a preset fusion algorithm to obtain fused precipitation information of the target river basin at the current moment.
Optionally, the method for determining the state variable at the initial moment specifically includes:
removing obstacles in the remote sensing image of the target river basin at the current moment to obtain a processed remote sensing image at the current moment; the obstacles include clouds, mountain shadows, ground surfaces, and vegetation;
and carrying out inversion extraction on the processed remote sensing image at the current moment by using a preset remote sensing inversion algorithm to obtain a state variable at the initial moment.
A flood forecast system, the system comprising:
the fused precipitation information acquisition module is used for acquiring fused precipitation information of the target river basin at the current moment; the fusion precipitation information is obtained by fusion according to multi-source remote sensing precipitation information, and the multi-source remote sensing precipitation information comprises: site precipitation information, radar precipitation information, satellite precipitation information and mode precipitation information;
the flood forecast result and state variable determining module is used for determining the flood forecast result of the target drainage basin at the current moment and the state variable of the target drainage basin at the current moment according to the fusion precipitation information at the current moment and the state variable of the target drainage basin at the last moment; the state variables include: water, evaporation capacity and soil humidity;
the state variable at the initial moment is determined according to the remote sensing image of the target river basin at the initial moment; the remote sensing image comprises: satellite images, radar images, and drone images.
Optionally, the fused precipitation information acquisition module specifically includes:
the multi-source remote sensing precipitation information acquisition unit is used for acquiring multi-source remote sensing precipitation information of a target river basin at the current moment;
the normalization unit is used for eliminating null values in the multi-source remote sensing precipitation information at the current moment, and then carrying out space-time resolution normalization processing to obtain a multi-source remote sensing precipitation data set with consistent space-time resolution at the current moment;
and the fusion precipitation information determining unit is used for fusing the multisource remote sensing precipitation data sets with consistent space-time resolution at the current moment by utilizing a preset fusion algorithm to obtain fusion precipitation information of the target river basin at the current moment.
Optionally, the flood forecast result and state variable determining module includes: an initial state variable determining sub-module; the initial state variable determining submodule specifically comprises:
the processing unit is used for removing obstacles in the remote sensing image of the target river basin at the current moment to obtain a processed remote sensing image at the current moment; the obstacles include clouds, mountain shadows, ground surfaces, and vegetation;
the initial state variable determining unit is used for carrying out inversion extraction on the processed remote sensing image at the current moment by using a preset remote sensing inversion algorithm to obtain a state variable at the initial moment.
An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the methods as described above.
A storage medium having stored thereon a computer program which, when executed by a processor, implements a method as described above.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses a flood forecasting method, a flood forecasting system, electronic equipment and a storage medium, wherein the flood forecasting method comprises the following steps: acquiring fusion precipitation information of a target river basin at the current moment; fusion precipitation information is obtained according to multisource remote sensing precipitation information fusion, and multisource remote sensing precipitation information comprises: site precipitation information, radar precipitation information, satellite precipitation information and mode precipitation information; according to the fusion precipitation information at the current moment and the state variable of the target river basin at the last moment, determining a flood forecast result of the target river basin at the current moment and the state variable of the target river basin at the current moment; the state variables include: water, evaporation capacity and soil humidity; the state variable at the initial moment is determined according to the remote sensing image of the target river basin at the initial moment; the remote sensing image includes: satellite images, radar images, and drone images. According to the invention, the rainfall information is fused, and the state variable of the last moment is adopted to infer the flood forecasting result of the current moment, so that the accuracy and precision of flood forecasting are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a flood forecasting method provided in embodiment 1 of the present invention;
fig. 2 is a schematic structural diagram of a flood forecast system according to embodiment 2 of the present invention;
FIG. 3 is a frame diagram of a flood forecast system;
FIG. 4 is a schematic diagram of a sky-ground multi-source remote sensing information fusion module;
FIG. 5 is a schematic view of meridian river basin fusion precipitation
FIG. 6 is a schematic diagram of a state variable multi-source remote sensing inversion module;
FIG. 7 is a schematic diagram of the result of inversion of soil humidity in meridian river basin;
FIG. 8 is a schematic diagram of a configuration module according to an assimilation algorithm;
FIG. 9 is a schematic diagram of a data assimilation module according to a model;
fig. 10 is a schematic diagram of verification results of a meridian river basin one-field flood process.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a flood forecasting method, a system, electronic equipment and a storage medium, aims to improve the accuracy and precision of flood forecasting, and can be applied to the technical fields of hydrology and remote sensing.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Example 1
Fig. 1 is a flow chart of a flood forecasting method according to embodiment 1 of the present invention. As shown in fig. 1, the flood forecasting method in this embodiment includes:
step 101: acquiring fusion precipitation information of a target river basin at the current moment; fusion precipitation information is obtained according to multisource remote sensing precipitation information fusion, and multisource remote sensing precipitation information comprises: site precipitation information, radar precipitation information, satellite precipitation information and mode precipitation information.
Step 102: according to the fusion precipitation information at the current moment and the state variable of the target river basin at the last moment, determining a flood forecast result of the target river basin at the current moment and the state variable of the target river basin at the current moment; the state variables include: water, evaporation capacity and soil humidity;
the state variable at the initial moment is determined according to the remote sensing image of the target river basin at the initial moment; the remote sensing image includes: satellite images, radar images, and drone images.
As an alternative embodiment, step 101 specifically includes:
and acquiring multisource remote sensing precipitation information of the target river basin at the current moment.
And removing null values in the multi-source remote sensing precipitation information at the current moment, and performing space-time resolution normalization processing to obtain a multi-source remote sensing precipitation data set with consistent space-time resolution at the current moment.
And fusing the multisource remote sensing precipitation data sets with consistent space-time resolution at the current moment by using a preset fusion algorithm to obtain fused precipitation information of the target river basin at the current moment.
As an alternative embodiment, the method for determining the state variable at the initial time specifically includes:
removing obstacles in the remote sensing image of the target river basin at the current moment to obtain a processed remote sensing image at the current moment; obstacles include clouds, mountain shadows, ground surfaces, and vegetation.
And carrying out inversion extraction on the processed remote sensing image at the current moment by using a preset remote sensing inversion algorithm to obtain a state variable at the initial moment.
Example 2
Fig. 2 is a schematic structural diagram of a flood forecast system according to embodiment 2 of the present invention. As shown in fig. 2, the flood forecast system in the present embodiment includes:
the fused precipitation information acquisition module 201 is configured to acquire fused precipitation information of a target drainage basin at a current moment; fusion precipitation information is obtained according to multisource remote sensing precipitation information fusion, and multisource remote sensing precipitation information comprises: site precipitation information, radar precipitation information, satellite precipitation information and mode precipitation information.
A flood forecast result and state variable determining module 202, configured to determine a flood forecast result of the target basin at the current time and a state variable of the target basin at the current time according to the fused precipitation information at the current time and the state variable of the target basin at the previous time; the state variables include: water, evaporation capacity and soil humidity;
the state variable at the initial moment is determined according to the remote sensing image of the target river basin at the initial moment; the remote sensing image includes: satellite images, radar images, and drone images.
As an alternative embodiment, the fused precipitation information acquisition module 201 specifically includes:
the multi-source remote sensing precipitation information acquisition unit is used for acquiring multi-source remote sensing precipitation information of the target river basin at the current moment.
And the normalization unit is used for eliminating null values in the multi-source remote sensing precipitation information at the current moment, and then carrying out space-time resolution normalization processing to obtain a multi-source remote sensing precipitation data set with consistent space-time resolution at the current moment.
And the fusion precipitation information determining unit is used for fusing the multisource remote sensing precipitation data sets with consistent space-time resolution at the current moment by utilizing a preset fusion algorithm to obtain fusion precipitation information of the target river basin at the current moment.
As an alternative embodiment, the flood forecast result and state variable determination module 202 comprises: an initial state variable determining sub-module; the initial state variable determining submodule specifically comprises:
the processing unit is used for removing obstacles in the remote sensing image of the target river basin at the current moment to obtain a processed remote sensing image at the current moment; obstacles include clouds, mountain shadows, ground surfaces, and vegetation.
The initial state variable determining unit is used for carrying out inversion extraction on the processed remote sensing image at the current moment by using a preset remote sensing inversion algorithm to obtain a state variable at the initial moment.
Specifically, as shown in fig. 3, the flood forecast system includes:
(1) Sky-ground multi-source remote sensing information fusion module:
(1) precipitation data acquisition center: firstly, according to the source information and the type information of the precipitation needed by fusion precipitation, a command is sent to a plurality of download buttons of different precipitation; secondly, the download button acquires multi-source precipitation data in real time according to download links of precipitation of different sources and stores the multi-source precipitation data in a local server, and after the data acquisition is completed, the download button transmits precipitation information to the data processing button through the transmission button; finally, the data processing button eliminates invalid values and null values of the multi-source precipitation data, and performs normalization processing on the precipitation data according to the characteristics of different precipitation data to obtain a multi-source precipitation data set with consistent time resolution and spatial resolution.
(2) Multisource precipitation fuses center: according to the space variability of the underlying surface of different watercourses and the characteristics of precipitation from different sources, a reconfiguration technology is adopted to select a proper fusion algorithm from a plurality of fusion algorithms (an optimal interpolation method, a Kalman filtering method, a dynamic Bayesian method, wavelet analysis, a probability density function, a neural network and the like) to perform multi-source precipitation fusion so as to obtain fusion precipitation with high space-time resolution.
(2) The state variable multisource remote sensing inversion module comprises:
(1) an image acquisition center: firstly, the center sends an instruction to a memory button for locally storing different images (satellite images, radar images and unmanned aerial vehicle images), the memory button receives the instruction and displays the different images, the displayed images can judge whether the images are absent at the moment, if the images are absent, the images which are not displayed at the moment are not displayed, and the corresponding images which are not displayed at the moment do not need subsequent processing; secondly, after the image display is finished, the image processing center sends an instruction to the extraction button, and according to the spatial position of the research drainage basin in the image, the image range of the research area is extracted, so that the calculated amount of quality control and inversion of the subsequent remote sensing image is reduced; and finally, the image processing center sends an instruction to the anomaly analysis button, and the instruction judges whether the image is in abnormal conditions such as cloud shielding, mountain shadow and the like.
(2) If the abnormal condition exists, executing the steps A and B; if no abnormal condition exists, executing the step B;
step A: and removing the influence of clouds, mountain shadows, earth surfaces, vegetation roughness and the like according to the acquired remote sensing images, and performing preliminary remote sensing image quality control.
And (B) step (B): different basin state variables are subjected to inversion extraction by adopting different remote sensing inversion algorithms, and continuous basin state variables (water body, evaporation, soil humidity and the like) are obtained.
(3) Assimilation algorithm configuration module:
(1) the first judging module is used for judging which specific basin state variables (water body, soil humidity, evaporation and the like) are assimilated.
(2) The second judging module is used for judging whether assimilation is needed or not according to the threshold value of the state variable of the river basin; if assimilation is needed, a third judging module is entered.
(3) And the third judging module is used for carrying out configuration and selection of an assimilation algorithm based on the environment-aware reconfiguration technology, selecting a robust and proper assimilation algorithm and carrying out assimilation of the state variable.
(4) Model-data assimilation module: the method is used for continuously updating and improving state variables of the hydrologic model at the current moment and calculating flood forecast results at the next moment.
(1) A first execution module: the method is used for constructing a model and constructing a hydrological model applicable to a research river basin.
(2) And a second execution module: and (3) finishing calculation of the hydrologic model at the current moment, suspending calculation of the hydrologic model (when the hydrologic model is not completely calculated), and storing the state variable generated at the current moment of the hydrologic model.
(3) And a third execution module: the analysis value of the state variable is generated by the assimilation algorithm configuration module through the state variable generated by the hydrologic model and the actually measured state variable.
(4) And a fourth execution module: if the state variable analysis value at the current moment exists, the file restarting hydrological model is controlled to calculate the next moment until the calculation of all time sequences is completed; if the state variable analysis value at the current moment does not exist, model-data assimilation is ended.
Taking a tributary meridian river in the Han river basin as an example, the first-class tributary of the north shore on the upstream side of the Han river in the meridian river is in the geographic position of 33 degrees 18-33 degrees 44 'of north latitude and 107 degrees 51-108 degrees 30' of east longitude. Meridian river belongs to mountain stream river, mainly flows through deep mountain area of Qinling mountain, and has sector shape. The whole river length is 161km, the river basin area is 3028km2, and the average river channel ratio is reduced by 5.44 per mill. The meridian river is formed by merging an upstream pepper river, pu He, a Wenshui river and a downstream Changan river. The total length of the Wenshui river above the three river ports (the intersection of the pepper river, pu He and the Wenshui river) is 106km, the river basin area is 1094km2, and the average river channel ratio is reduced by 9.3 per mill; the length of the pepper stream is 70km, the area of the river basin is 596km2, and the average ratio of the river channel is reduced by 18.7 per mill; pu Hechang 58km, a river area of 496km2 and an average river channel ratio drop of 26.6%. The peak elevation of the main peak of the Qinling mountain is 2965 meters, which is the highest peak of the river basin. The vegetation in the river basin is good, the forest is thick, and the forest coverage rate reaches 70%. Due to the blocking effect of the Qinling mountain, humid air from the south and southeast in summer carries a lot of water vapor, resulting in frequent disastrous weather. The rainfall in the watershed has obvious seasonality, and the summer accounts for 46% of the annual rainfall, while the winter rainfall accounts for only 3% of the annual rainfall. The annual average rainfall, evaporation capacity and runoff depth of the river basin are respectively as follows: 900mm, 500mm, 400mm. Uneven distribution of rainfall season, and steep topography are easy to cause mountain floods and seasonal floods.
The system for constructing the meridian river basin based on sky-ground multidimensional sensing fusion and river basin state inversion assimilation comprises the following modules:
(1) Sky-ground multi-source remote sensing information fusion module (as shown in fig. 4):
(1) precipitation data acquisition center: firstly, according to the source required by the fusion of precipitation and the type information of the precipitation, a command is sent to a plurality of download buttons of different precipitation; secondly, the download button acquires multi-source precipitation data in real time according to download links of precipitation of different sources and stores the multi-source precipitation data in a local server, and after the data acquisition is completed, the download button transmits precipitation information to the data processing button through the transmission button; finally, the data processing button eliminates invalid values and null values of the multi-source precipitation data, and performs normalization processing on the precipitation data according to the characteristics of different precipitation data to obtain a multi-source precipitation data set with consistent time resolution and spatial resolution.
(2) Multisource precipitation fuses center: according to the characteristics of the underlying surface of the meridian river basin and the characteristics of precipitation from different sources, a reconfiguration technology is adopted to select a proper fusion algorithm from a plurality of fusion algorithms (an optimal interpolation method, a Kalman filtering method, a dynamic Bayesian, wavelet analysis, a probability density function, a neural network and the like), and the meridian river basin multi-source precipitation fusion is carried out to obtain a fusion precipitation data set with high space-time resolution, as shown in figure 5.
(2) State variable multisource remote sensing inversion module (shown in fig. 6):
(1) an image acquisition center: firstly, the center sends an instruction to a memory button for locally storing different images (satellite images, radar images and unmanned aerial vehicle images), the memory button receives the instruction and displays the different images, the displayed images can judge whether the images are absent at the moment, if the images are absent, the images which are not displayed at the moment are not displayed, and the corresponding images which are not displayed at the moment do not need subsequent processing; secondly, after the image display is completed, the image processing center sends an instruction to an extraction button, and according to the spatial position of the research river basin in the image, the image range of the meridian river basin is extracted, so that the calculated amount of quality control and inversion of the subsequent remote sensing image is reduced; and finally, the image processing center sends an instruction to the anomaly analysis button, and the instruction judges whether the image is in abnormal conditions such as cloud shielding, mountain shadow and the like.
(2) If the abnormal condition exists, executing the steps A and B; if no abnormal condition exists, executing the step B;
step A: and removing the influence of clouds, mountain shadows, earth surfaces, vegetation roughness and the like according to the acquired remote sensing images, and performing preliminary remote sensing image quality control.
And (B) step (B): inversion extraction is performed on the soil humidity of the noon river basin, and continuous soil humidity data are obtained, as shown in fig. 7.
(3) Assimilation algorithm configuration module (as shown in fig. 8):
(1) the first judgment module is executed first, and is used for judging which specific river basin state variables (water body, evaporation, soil humidity and the like) are assimilated, and the soil humidity is taken as an example to assimilate in meridian river basins.
(2) The second judging module judges whether assimilation is needed according to the soil humidity value of the meridian river basin, the meridian river basin belongs to a humid area, and judging thresholds of saturated water content, field water holding capacity and withering water content are respectively as follows: w (W) m ≥120、W m <120、W m Less than or equal to 40, and no assimilation is needed when the soil humidity is under the condition of saturated water content; and when the soil humidity is in the conditions of field water holding capacity and withering water content, assimilating, and entering a third judging module.
(3) And the third judging module is used for carrying out configuration and selection of an assimilation algorithm based on an environment-aware reconfiguration technology, selecting a robust and suitable assimilation algorithm and carrying out assimilation of the soil humidity of the meridian river basin.
(4) Model-data assimilation module (as shown in fig. 9): the method is used for continuously updating and improving the state variable of the current moment of the hydrologic model and calculating the next moment.
(1) A first execution module: the method is used for constructing a model and constructing a hydrological model applicable to the meridian river basin.
(2) And a second execution module: and (3) finishing calculation of the hydrologic model at the current moment, suspending calculation of the hydrologic model (when the hydrologic model is not completely calculated), and storing the soil humidity generated by the hydrologic model at the current moment.
(3) And a third execution module: and generating an analysis value of a state variable through an assimilation algorithm configuration module by the soil humidity generated by the hydrologic model and the actually measured site and the remote sensing inversion soil humidity.
(4) And a fourth execution module: if the state variable analysis value at the current moment exists, the file restarting hydrological model is controlled to calculate the next moment until the calculation of all time sequences is completed; if the state variable analysis value at the current moment does not exist, model-data assimilation is ended.
(5) Typical watershed verification module: verification analysis of the system calculation result (shown in fig. 10) is performed based on the measured data, and is used for evaluating the accuracy and applicability of the system.
Example 3
The invention also provides an electronic device, comprising:
one or more processors.
A storage device having one or more programs stored thereon.
The one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method as in embodiment 1.
Example 4
The present invention also provides a storage medium having stored thereon a computer program which, when executed by a processor, implements a method as described in embodiment 1
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the above examples being provided only to assist in understanding the device and its core ideas of the present invention; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (4)
1. A flood forecasting method, the method comprising:
acquiring fusion precipitation information of a target river basin at the current moment; the fusion precipitation information is obtained by fusion according to multi-source remote sensing precipitation information, and the multi-source remote sensing precipitation information comprises: site precipitation information, radar precipitation information, satellite precipitation information and mode precipitation information;
according to the fusion precipitation information at the current moment and the state variable of the target river basin at the last moment, determining a flood forecast result of the target river basin at the current moment and the state variable of the target river basin at the current moment; the state variables include: water, evaporation capacity and soil humidity;
the state variable at the initial moment is determined according to the remote sensing image of the target river basin at the initial moment; the remote sensing image comprises: satellite images, radar images, and drone images;
the obtaining the fused precipitation information of the target river basin at the current moment specifically comprises the following steps:
acquiring multisource remote sensing precipitation information of a target river basin at the current moment;
removing null values in the multi-source remote sensing precipitation information at the current moment, and performing space-time resolution normalization processing to obtain a multi-source remote sensing precipitation data set with consistent space-time resolution at the current moment;
utilizing a preset fusion algorithm to fuse multisource remote sensing precipitation data sets with consistent space-time resolution at the current moment to obtain fused precipitation information of the target river basin at the current moment;
the method for determining the state variable at the initial moment specifically comprises the following steps:
removing obstacles in the remote sensing image of the target river basin at the current moment to obtain a processed remote sensing image at the current moment; the obstacles include clouds, mountain shadows, ground surfaces, and vegetation;
and carrying out inversion extraction on the processed remote sensing image at the current moment by using a preset remote sensing inversion algorithm to obtain a state variable at the initial moment.
2. A flood forecast system, the system comprising:
the fused precipitation information acquisition module is used for acquiring fused precipitation information of the target river basin at the current moment; the fusion precipitation information is obtained by fusion according to multi-source remote sensing precipitation information, and the multi-source remote sensing precipitation information comprises: site precipitation information, radar precipitation information, satellite precipitation information and mode precipitation information;
the flood forecast result and state variable determining module is used for determining the flood forecast result of the target drainage basin at the current moment and the state variable of the target drainage basin at the current moment according to the fusion precipitation information at the current moment and the state variable of the target drainage basin at the last moment; the state variables include: water, evaporation capacity and soil humidity;
the state variable at the initial moment is determined according to the remote sensing image of the target river basin at the initial moment; the remote sensing image comprises: satellite images, radar images, and drone images;
the fused precipitation information acquisition module specifically comprises:
the multi-source remote sensing precipitation information acquisition unit is used for acquiring multi-source remote sensing precipitation information of a target river basin at the current moment;
the normalization unit is used for eliminating null values in the multi-source remote sensing precipitation information at the current moment, and then carrying out space-time resolution normalization processing to obtain a multi-source remote sensing precipitation data set with consistent space-time resolution at the current moment;
the fusion precipitation information determining unit is used for fusing multisource remote sensing precipitation data sets with consistent space-time resolution at the current moment by using a preset fusion algorithm to obtain fusion precipitation information of the target river basin at the current moment;
the flood forecast result and state variable determining module comprises: an initial state variable determining sub-module; the initial state variable determining submodule specifically comprises:
the processing unit is used for removing obstacles in the remote sensing image of the target river basin at the current moment to obtain a processed remote sensing image at the current moment; the obstacles include clouds, mountain shadows, ground surfaces, and vegetation;
the initial state variable determining unit is used for carrying out inversion extraction on the processed remote sensing image at the current moment by using a preset remote sensing inversion algorithm to obtain a state variable at the initial moment.
3. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method as recited in claim 1.
4. A storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the method as claimed in claim 1.
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| CN106991278A (en) * | 2017-03-21 | 2017-07-28 | 武汉大学 | It is a kind of to gather precipitation forecast and the coupling process of real-time flood probability forecast |
| CN109814178A (en) * | 2018-12-25 | 2019-05-28 | 河海大学 | Hydrological probability forecasting procedure based on Copula- Model Condition processor |
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| CN106991278A (en) * | 2017-03-21 | 2017-07-28 | 武汉大学 | It is a kind of to gather precipitation forecast and the coupling process of real-time flood probability forecast |
| CN109814178A (en) * | 2018-12-25 | 2019-05-28 | 河海大学 | Hydrological probability forecasting procedure based on Copula- Model Condition processor |
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| Title |
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| 国产多源降水融合及其在小流域暴雨山洪预报中的应用;赵悬涛;刘昌军;文磊;王文川;;中国农村水利水电(10);全文 * |
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